class_docstr: Class for preprocessing Time Series Forecasting Experiments.
class_docstr_tok: ["Class","for","preprocessing","Time","Series","Forecasting","Experiments","."]
docstr: Add feature engineering steps for the data.
  
  Parameters
  ----------
  fe_exogenous : list
      Feature engineering transformations to be applied to exogenous variables.
  target : bool, optional
      If True, feature engineering steps are added to the target variable steps
      (This is not implemented yet - see note in _feature_engineering)
      If False, feature engineering steps are added to the exogenous variable steps,
      by default True
  
  Raises
  ------
  ValueError
      `fe_exogenous` is not of the correct type.
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code: def _add_feat_eng_steps(
      self, fe_exogenous: list, target: bool = True
  ):
      """Add feature engineering steps for the data.
  
      Parameters
      ----------
      fe_exogenous : list
          Feature engineering transformations to be applied to exogenous variables.
      target : bool, optional
          If True, feature engineering steps are added to the target variable steps
          (This is not implemented yet - see note in _feature_engineering)
          If False, feature engineering steps are added to the exogenous variable steps,
          by default True
  
      Raises
      ------
      ValueError
          `fe_exogenous` is not of the correct type.
      """
      type_ = "Target" if target else "Exogenous"
      self.logger.info(
          f"Set up feature engineering for {type_} variable(s)."
      )
  
      if not isinstance(fe_exogenous, list):
          raise ValueError(
              f"{type_} Feature Engineering input must be of a List or sktime transformers."
              f"You provided {fe_exogenous}"
          )
  
      if target:
          # This is not implemented yet - see note in _feature_engineering
          pass
      else:
          self.transformer_steps_exogenous.extend(fe_exogenous)
  
file_path: pycaret/pycaret/internal/preprocess/time_series/forecasting/preprocessor.py
imports: from typing import Optional, Union
  from sklearn.preprocessing import MaxAbsScaler, MinMaxScaler, RobustScaler, StandardScaler
  from sktime.transformations.compose import ColumnwiseTransformer
  from sktime.transformations.series.adapt import TabularToSeriesAdaptor
  from sktime.transformations.series.boxcox import BoxCoxTransformer, LogTransformer
  from sktime.transformations.series.cos import CosineTransformer
  from sktime.transformations.series.exponent import ExponentTransformer, SqrtTransformer
  from sktime.transformations.series.impute import Imputer
  from pycaret.utils.time_series import TSExogenousPresent
label_desc: Use image node_id 8 for calling the TSForecastingPreprocessor obj's underlying member method code with example usage: obj._add_feat_eng_steps(fe_exogenous, target) without return types